Llama

Llama

Description of the Llama neural network

Llama is a family of open-source large language models developed by Meta. The models are available in various versions, including Scout, Maverick, Behemoth, as well as earlier versions 3.1, 3.2, and 3.3. They support multimodal data processing: text, images, audio, and video. The model architecture is based on mixture of experts (MoE), which makes it possible to distribute computing resources efficiently. Llama can generate code, translate texts, analyze large volumes of information, and carry on conversations. The models are distributed under a freemium model: they are available both for local use with open weights and via cloud APIs for commercial deployment.

Llama characteristics

Characteristic	Value
Developer	Meta
Type	Multimodal language model (text, images, audio, video)
Architecture	Mixture of Experts (MoE)
Versions	Scout, Maverick, Behemoth, 3.1, 3.2, 3.3
Context window	Up to 10 million tokens (Scout), 1 million tokens (Maverick), 128K tokens (3.1)
Number of parameters	Up to 2 trillion (Behemoth), 400 billion (Maverick), 109 billion (Scout)
Active parameters per token	17 billion (Scout, Maverick)
Training	15–40 trillion tokens, up to 100,000 Nvidia H100 accelerators
Supported languages	20+ languages, including Russian
Translation accuracy	95%
Release date	April 5, 2025 (Llama 4), July 23, 2024 (Llama 3.1)
License	Open Source (Llama 4 Community License / Meta Llama 3.1 Community License)
Distribution model	Freemium

Who is the Llama neural network suitable for?

Developers and programmers

Llama is aimed at developers who can download models for local use, integrate them via API, and fine-tune them for their own tasks. The model is suitable for building applications, chatbots, and virtual assistants.

Researchers and data analysts

The model is useful for researchers working with large volumes of information and for data analysts who need to process text, images, and audio.

Business and content creators

Llama is suitable for businesses in marketing, customer service, and content creation. Companies can use it to automate routine tasks and generate materials.

How to use the Llama neural network?

Local use

To run the model locally, download it from the official Llama website. For example, Scout requires about 4 GB of free space. Then install Python 3.10 or higher and run the script via the terminal. A computer with a GPU with at least 16 GB of video memory is recommended.

Using the API

Llama is available through cloud services such as OpenRouter, Together AI, Fireworks AI, GroqCloud, AWS Bedrock, and Azure. To get started, register on your chosen platform and obtain an API key.

Web application

Users can sign up on the Llama website and use the web interface to work with the model. This does not require local installation and is suitable for quick testing.

Key features of Llama

Text and code generation

The model can generate coherent texts and program code. Maverick generates code in 2 seconds, and its code generation speed is 40% higher than DeepSeek v3.1.

Multimodal processing

Llama analyzes text, images, and audio. Version 3.2 supports multimodal processing, and Llama 4 was originally designed as a multimodal system.

Working with long documents

The model can process contexts of up to 10 million tokens, allowing it to analyze books of up to 5,000 pages. Scout is designed for working with large volumes of text.

Translation and language support

Llama supports 20 languages, including Russian, with 95% translation accuracy. The model is trained on data in 200 languages.

Advantages of Llama

Record-breaking context window

A context window of 10 million tokens (80 times larger than Llama 3) makes it possible to process huge volumes of information in a single request.

High performance

The model outperforms GPT-4o in 7 benchmarks and runs 30% faster thanks to FP8 training. It attracted 500,000 developers in the first week after release.

Free access and open source

Llama is completely free without restrictions, including all versions. The open source code allows the model to be customized for specific tasks.

Cost efficiency

The MoE architecture reduces inference costs: although Scout has 109 billion parameters, only 17 billion are active per token. This makes the model cheaper than comparable alternatives at similar quality.

Disadvantages of Llama

Regional restrictions

Access to the service may be restricted in certain regions depending on local legislation. Users in such regions may need to use alternative access methods.

License restrictions

The Llama 4 Community License is not OSI-compatible and limits commercial use to 700 million monthly active users. Larger companies must request a separate license.

High resource requirements

Running large versions locally (for example, Llama 3.1 405B) requires powerful hardware: at least 8 A100 80GB GPUs. Even less demanding versions need a GPU with at least 16 GB.

What tasks does Llama solve?

Data processing and analysis

The model is suitable for analyzing large volumes of text, books, and documents of up to 5,000 pages, as well as for processing images and audio.

Programming and coding

Llama generates code, automates development, and achieves strong benchmark results: HumanEval 89.0% (version 3.1 405B) and 72.6% (version 3.1 8B).

Translation and multilingual tasks

The model translates text into 20 languages and supports conversations in multiple languages.

Content creation

Llama generates texts, articles, and creative content, and can also be used to create chatbots and virtual assistants.

Llama pricing

Free tier

All versions of Llama are completely free without restrictions. For local use, the model can be downloaded from the official website. The web application is also free of charge.

Paid APIs

When using the API, the cost depends on the provider. For example, on OpenRouter: Scout — $0.08 per 1 million input tokens, $0.30 per 1 million output tokens; Maverick — $0.15 per 1 million input tokens, $0.60 per 1 million output tokens. Llama 3.1 8B costs $0.20 per 1 million tokens for both input and output.

Trial access

Some cloud providers, such as Together.ai, provide limited free access to the model. A demo is also available on HuggingFace Spaces.

Llama terms of use

Registration

Using the web application requires registration on the Llama website. When working via API, registration with the chosen cloud provider is required.

License restrictions

For commercial use, the Meta Llama 3.1 Community License or Llama 4 Community License applies. Commercial use is permitted for companies with an audience of up to 700 million users. Larger companies must request a separate license.

Regional restrictions

Access may be restricted in some countries depending on local regulations. In such cases, alternative access methods may be required.

Llama availability

Web application and API

Llama is available as a web application on the official website. The model is also available through APIs from cloud providers: OpenRouter, Together AI, Fireworks AI, GroqCloud, AWS Bedrock, Azure, and Google Vertex AI.

Local use

Models can be downloaded from the official website or HuggingFace. For local use, a computer with a GPU of at least 16 GB (for Scout) or 8×A100 80GB (for Llama 3.1 405B) is required. Local use does not require an internet connection.

Supported languages

Llama supports 20+ languages, including Russian. The model is trained on data in 200 languages.

How is Llama different from alternatives?

Context window

Llama 4 Scout has a record context window of 10 million tokens, significantly surpassing alternatives: Gemini 3.1 Pro (2 million tokens) and Claude Opus 4.6 (1 million tokens).

Performance

The model outperforms ChatGPT and DeepSeek in benchmarks. Its code generation speed is 40% higher than DeepSeek v3.1. Llama 4 Maverick outperforms GPT-4o and Gemini 2.0 Flash in a number of metrics.

Open source

Unlike proprietary models (GPT-4, Claude), Llama has open source code and allows the model to be fine-tuned for specialized tasks. This makes it valuable for researchers and developers.

Cost

Llama is cheaper than alternatives at comparable quality. For example, Maverick costs $0.60 per 1 million output tokens, compared to $3 for Claude Sonnet 4.6. A completely free version is available for local use without restrictions.

Conclusion

Llama is a powerful family of open-source multimodal language models from Meta. Key advantages include a record context window of up to 10 million tokens, high performance surpassing GPT-4o and DeepSeek, completely free access, and open weights for local use. The model is suitable for developers, researchers, and businesses, handling tasks from data analysis to programming. The main limitations are related to license restrictions for large companies and potential regional availability restrictions.